Enhancing systematic review with machine learning modelling for global health research
Authors: Yuan Luo
Journal: The Lancet Digital Health (2022)

Systematic reviews and meta-analyses have long been adopted in global health research for aggregating existing studies. Although these analyses can synthesise existing evidence, additional insights from published studies can be unlocked by curating published studies and performing further modelling analysis. Yuan-Yuan Zhang and colleagues1Zhang Y-Y Sun Y-Q Chen J-J et al.Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysis.Lancet Digit Health. 2022; (published online Nov 21.)https://doi.org/10.1016/S2589-7500(22)00212-6Summary Full Text Full Text PDF Scopus (5) Google Scholar show that systematically reviewing, geolocating, and modelling published studies on spotted fever group rickettsioses (SFGR) can model infectious disease risks at a global scale. The authors reviewed literature on SFGR published between January, 1906, and March, 2021, and described the paucity of understanding on the spatial distribution, ecological niches, and clinical manifestations of SFGR worldwide. To address this challenge, the authors geolocated studies and categorised SFGR by source of detection (vectors, animals, or humans). Using the geolocated data, they analysed the relations between spatial distributions of vectors, rickettsiae species, and their corresponding clinical presentations. They then applied machine learning models to predict the distributions of 17 major SFGR species from potential environmental, ecoclimatic, and biological drivers. Based on model feature analysis, the authors obtained five spatial clusters of SFGR species responding to distinct environmental and ecoclimatic factors. Zhang and colleagues1Zhang Y-Y Sun Y-Q Chen J-J et al.Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysis.Lancet Digit Health. 2022; (published online Nov 21.)https://doi.org/10.1016/S2589-7500(22)00212-6Summary Full Text Full Text PDF Scopus (5) Google Scholar used geo-positioning to augment the reviewed studies with occurrence locations for studying geographical distribution patterns of rickettsiae species, vectors, and their impact on clinical presentations. New insights on disease transmission, spread, and outbreaks would be otherwise difficult to obtain but for the augmented spatial data. A study can have so-called leftover data that may lack power or annotation (eg, geo-annotation). Thus, data augmentation and pooling from existing studies for aggregated analysis can be a fruitful strategy to answer additional research questions. The genetics community are among early adopters (eg, ClinVar2Landrum MJ Lee JM Riley GR et al.ClinVar: public archive of relationships among sequence variation and human phenotype.Nucleic Acids Res. 2014; 42: D980-D985Crossref PubMed Scopus (1741) Google Scholar and OMIM3Amberger JS Bocchini CA Schiettecatte F Scott AF Hamosh A OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.Nucleic Acids Res. 2015; 43: D789-D798Crossref PubMed Scopus (1290) Google Scholar); Zhang and colleagues demonstrated that this strategy can also benefit global health research. However, their exclusion of non-exact geocoded data might bias towards studies with better quality data and meta-data. As low-resource areas often have fewer studies and sometimes with lower quality than high-resource areas,4Chen W Xia C Zheng R et al.Disparities by province, age, and sex in site-specific cancer burden attributable to 23 potentially modifiable risk factors in China: a comparative risk assessment.Lancet Glob Health. 2019; 7: e257-e269Summary Full Text Full Text PDF PubMed Scopus (145) Google Scholar this could exacerbate the existing disparity issues. Thus, care must be taken for data augmentation to avoid exacerbating health inequities. The study offers important evidence for understanding climate change as a public health crisis5Mueller J Long Roche K Lieber D Decarbonizing the U.S. Health Sector.N Engl J Med. 2022; 386: 1003-1004Crossref PubMed Scopus (1) Google Scholar (eg, contribution of annual mean temperature to SFGR transmission and outbreak). The study also provides evidence linking the spectrum of vectors to increasing SFGR infections, and calls for improved awareness, diagnosis, and surveillance in high-risk regions. These results call for urgent attention from the wider infectious disease research community on the links between climate change and the growing spectrum of vectors and increasing disease infections. Time is of the essence in responding to climate and health change,6Watts N Amann M Ayeb-Karlsson S et al.The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health.Lancet. 2018; 391: 581-630Summary Full Text Full Text PDF PubMed Scopus (658) Google Scholar but prospective studies take time to conduct and inform actions. This study sets an example of leveraging existing literature to assess contributions from environmental and ecoclimatic factors to global health challenges. Generalising this study can enable rapid accumulation of crucial evidence in countering the toll of climate change on global health. On the other hand, this poses new requirements for publication reporting standards, eg, more complete meta-information including geolocation and study participant characteristics. Although publication databases can enhance their curation efforts to annotate such information, authors can also collectively ensure such meta-information be accurately reported in the first place. A two-pronged approach can better prepare publications to be analysis-ready, reporting data that continuously enable new questions to be asked and new insights to be discovered. The authors1Zhang Y-Y Sun Y-Q Chen J-J et al.Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysis.Lancet Digit Health. 2022; (published online Nov 21.)https://doi.org/10.1016/S2589-7500(22)00212-6Summary Full Text Full Text PDF Scopus (5) Google Scholar used machine learning to inform surveillance and guide targeted data collection. Existing global health systematic reviews occasionally apply modelling analysis to assess risk factor effects (eg, epidemiological modelling for carotid atherosclerosis7Song P Fang Z Wang H et al.Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study.Lancet Glob Health. 2020; 8: e721-e729Summary Full Text Full Text PDF PubMed Scopus (257) Google Scholar) but few apply machine learning. Using machine learning, Zhang and colleagues predicted the risk of potential SFGR occurrences at locations with little or no epidemiological investigation to guide future field investigation and surveillance. This is a first step towards proactive machine learning.8Luo Y Wunderink RG Lloyd-Jones D Proactive vs reactive machine learning in health care: lessons from the COVID-19 pandemic.JAMA. 2022; 327: 623-624Crossref PubMed Scopus (10) Google Scholar Conventional machine learning treats data as a stable asset, and faces challenges from incomplete data, data change (eg, the constantly evolving new variants of COVID-19) and bias or disparity9Obermeyer Z Powers B Vogeli C Mullainathan S Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453Crossref PubMed Scopus (1534) Google Scholar issues. For the changing environmental and ecoclimatic landscape of SFGR, proactive machine learning leverages models to identify weak spots in the data collection (eg, locations with little or no epidemiological investigation), and guides continuous targeted data collection (eg, future field investigation and surveillance). Using only average ecological predictors and cumulative occurrences, this study falls short of untangling the temporal evolution. More spatial-temporally refined data (eg, through the above two-pronged approach) can support training dynamic models to leverage both cross-sectional and longitudinal information to better discover data weak spots. This can sustain a virtuous cycle where better representative data will power more accurate models, which will in turn pinpoint data improvement needs for the next iteration. I declare no competing interests. Mapping the global distribution of spotted fever group rickettsiae: a systematic review with modelling analysisThe wide spectrum of vectors is contributing substantially to the increasing incidence of SFGR infections among humans. Awareness, diagnosis, and surveillance of SFGR infections should be improved in the high-risk regions, especially in areas where human infections are underreported. Full-Text PDF Open Access